Measuring caloric response: Comparison of different analysis techniques
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
INTRODUCTION: Electronystagmography (ENG) testing has been supplanted by newer techniques of measuring eye movement with infrared cameras (VNG). Most techniques of quantifying caloric induced nystagmus measure the slow phase velocity in some manner. Although our analysis is carried out by very experienced assessors, some systems have computer algorithms that have been "taught" to locate and quantify maximum responses. We wondered what differences in measurement might show up when measuring calorics using different techniques and systems, the relevance of this being that if there was a change in slow phase velocity between ENG and VNG testing when measuring caloric response, then normative data would have to be changed. There are also some subjective but important aspects of ENG interpretation which comment on the nature of the response (e.g. responses which might be "sporadic" or "scant"). METHODS: Our experiment compared caloric responses in 100 patients analyzed four different ways. Each caloric was analyzed by our old ENG system, our new VNG system, an inexperienced assessor and the computer algorithm, and data was compared. CONCLUSIONS: All four systems made similar measurements but our inexperienced assessor failed to recognize responses as sporadic or scant, and we feel this is a limitation to be kept in mind in the rural setting, as it is an important aspect of assessment in complex patients. Assessment of complex VNGs should be left to an experienced assessor.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it